Pricing is a complex optimization problem requiring multiple data inputs. In particular, the real estate industry has vast data that is often fragmented. Companies in the space, such as hotels and investors, have realized the power that real estate data holds. At Blueground, we are at the early stages of developing a predictive model to optimize our pricing. In particular, we are using internal and external signals to predict whether our units will get booked and ultimately give price suggestions. We recently started developing our pricing model and are excited to share what we have learned from this journey so far

1. Show up. Start living.
PyData Piraeus, How Machine Learning is Reshaping Pricing
September 27th, 2019

2. D A T A & B I
BLUEGROUND BI TEAM AND AREAS OF FOCUS
Strategy
Data & BI
Growth
People Ops
Primary focus areas
Pricing Asset selection
Sales
Channel
& Mkg
BD CX
Competition
Analytics and visualizations
City and individual KPIs

3. Define Goals
D A T A & B I
HOW ML IS RESHAPING PRICING OPTIMIZATION
Pricing
Challenges
Changing market
conditions
Different products
Understanding
consumer behavior
Collect Data Train Model Price Recommendation
Quantity &
Fluctuation of Data
Pricing Optimization Process

4. Q3-Q4 ‘19 onwards
Model-Driven &
Automated
D A T A & B I
BLUEGROUND PRICING APPROACH
Q1-Q2 ‘19
Analytics & Rules-Driven
Q3-Q4 ‘ 18
Human-driven & Manual
Initial price
Probability
to book
Price
recoms.
Price sync
PRICING MODEL
Optimization Goal
➢ Maximize Price
➢ Minimize Vacancy

5. Number of inquiries
BG unit availability
Number of bookings
Website traffic
Deal duration & source
D A T A & B I
DYNAMIC PRICING DATA INPUTS
Market Data
City Data
Unit Data
Lead Data

6. D A T A & B I
FIRST MODEL ITERATION (LEAD LEVEL)
Model Specifications
Models: XGBoost, light GBM, GBM
Advantage: Focused on computational speed, model performance and interpretability
Features: ~80 features organised in 4 main clusters
Interpretability: Elasticity of each lead to selected features
Goal: Predict probability of sales inquiry converting to booking

7. D A T A & B I
INTERPRETABILITY

8. D A T A & B I
OUTPUT

9. D A T A & B I
LEARNINGS
● Garbage In = Garbage Out
● Collect Data Early
● Productionize Early
● Invest in a Solid Testing & Production Environment

10. D A T A & B I
SECOND MODEL ITERATION (AGGREGATE LEVEL)
Model Specifications
Models: Combination of classification and regression
Features: Added features around market, availability, channel presence, top funnel
Goal: Predict probability of unit getting booked by day of vacancy & expected date

11. D A T A & B I
OUTPUT & NEXT STEPS
Next Steps
● New features
● Prediction interval
● Price elasticity
● A/B testing
● Assess model predictions
vs. user decisions

12. D A T A & B I
QUESTIONS & FOLLOW-UP
Thank you! Questions?
We’re always looking for new ideas and to grow our team. Contact:
● Yannis Sarantis - yanniss@theblueground.com
● Konstantinos Karagiannis - konstantinosk@theblueground.com
● Leonidas Tsolas - leonidas.tsolas@theblueground.com